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Record W2058153650 · doi:10.1115/detc2010-28239

Control of a Pneumatic Gantry Robot With Adaptive Neural Network Compensation

2010· article· en· W2058153650 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIterative Learning Control Systems
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsControl theory (sociology)Compensation (psychology)Artificial neural networkRobotController (irrigation)Tracking (education)Computer scienceControl engineeringAdaptive controlEnhanced Data Rates for GSM EvolutionGantry craneControl systemEngineeringControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

In a previous paper, the application of a pneumatic gantry robot to contour tracking was examined. A hybrid controller was structured to control the contact force and the tangential velocity, simultaneously. Performance was found to be limited by system lag and Coulomb friction. A neural network (NN) compensator was subsequently developed to counter both effects. Simulation results for straight and curved edge workpieces demonstrated the effectiveness of the NN compensator. This paper validates the results experimentally, highlights the tuning issues associated with an adaptive NN compensator, and confirms the capabilities of a pneumatic gantry robot.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.367
Threshold uncertainty score0.386

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.180
Teacher spread0.175 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations1
Published2010
Admission routes2
Has abstractyes

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